An evaluation of the accelerated expectation maximization algorithms for single-photon emission tomography image reconstruction

We previously reported that brain single-photon emission tomography (SPET) images could be improved by using an attenuation coefficient map constructed with transmission data and the iterative expectation maximization (EM) algorithm. However, the conventional EM algorithm (CEM) typically requires 30–80 iterations to provide acceptable results, limiting its clinical applicability. Several methods have been proposed to accelerate the EM algorithm. The purpose of this study was to search for a practical method for accelerating the EM algorithm. The methods investigated here include the accelerated EM algorithm (ACEM) using additive correction, ACEM using multiplicative correction, and Tanaka's filtered iterative reconstruction method (FIR). These methods were assessed by simulated SPET studies of a phantom incorporating nonuniform attenuation and by reference to clinical brain SPET data. In the simulation studies, the above methods were evaluated by using three parameters (root mean square error, log likelihood value, and contrast recovery coefficient); the results showed that FIR had an advantage over other methods in terms of all parameters. The results obtained using the clinical data demonstrated that FIR could reconstruct acceptable images in only five iterations. These results show that FIR offers significant advantages over CEM or other ACEMs, indicating that FIR can make the EM algorithm practical for clinical use in SPET.